Entities, such as consumers and digital publishers, are constantly searching for approaches to discover and promote the most relevant content at the right time. Advertisers are constantly striving to reach their followers and fans with their messaging and do that via the publishing of relevant content.
Current approaches for determining relevancy in the real-time data stream generally rely on the manual creation of search queries across a set of real-time content and filtering the results based upon proximity to the keywords in the search query. In addition, other factors that may be used to filter real-time content include the relevancy to a user, the time the content was published, the engagement of the content published, or the influence of the content publisher. These approaches result in a basic match of the real-time content to the query and provide basic capabilities but do little to provide deep differentiation among similar content. Additionally, these approaches lack an ability to differentiate content based upon real-time popularity or the relative change in popularity. These approaches further lack and lag in understanding the dynamic and ever-changing language used in the real-time web, and have an incomplete vocabulary or data set to understand the nature of the meaning of the keywords in their queries. Limiting the real-time data to a finite set of characters has a causal effect on the language and words used by consumers, leaving traditional natural-language processing or keyword-search methodologies limited in efficacy.
To further complicate the current problem, real-time data is growing exponentially in terms of data and content produced. The growth in quantity and diversity of such data is making discoverability a more acute problem for consumers, not to mention creating a difficult challenge in determining the relevance of individual pieces of content.